% As says the introduction on the website itself (http://movecommons.org/), "Move Commons is a simple tool for connecting potential volunteers and contributors to initiatives, collectives and NGOs."

% Move Commons allows the collective/initiative:

% -  To help others to understand our work and approach;
% -  To find our work easier;
% -  To find other collectives with common interests;
% -  To attract volunteers to our collective.

% Move Commons uses the MC ontology in a semantic search engine. This will allow to search and find initiatives/collectives searching by keyword, by specific MC labels, by location, etc., for example: “non-profit grassroots initiatives working on alternative education in Beirut” .

% We also integrate the MC ontology in the LOD (Linking Open Data). This allows the integration with other semantic platforms and to easily build services for the network of MC-labelled initiatives.

% On its website says that Move Commons uses dbpedia (an ontology based on the Wikipedia articles, for more information we can have a look at http://dbpedia.org/) as a dictionary for the initiative’s keywords and this allows that for each wikipedia concept we can have the initiatives/collectives that work on that issue. But actually and sadly the truth is that people always add keywords as they want so finally we have cases such as:

% - Initiatives which are very similar but with different keywords;
% - Or on the other side, initiatives which are different but have similar keywords.

% This situation makes it a kind of difficult to discover the similarity among initiatives and in the future we have to look it as a big risk to make the Move Commons dream (To help others to understand our work and approach; to find our work easier; to find other collectives with common interests; to attract volunteers to our collective, etc.) dashed. For this reason, we consider it as an important job to have a certain technique to compare initiatives characterized by a set of keywords based on semantic similarities among them.

The use of semantic comparison is a desirable feature of any information retrieval system. If computers would be able to apply it correctly, a search of the words \textit{black, bird} could match a resource that is defined as a \textit{crow}. Our work proposes a technique to measure the semantic similarity among two elements identified by a collection of keywords. This approach is implemented in an available prototype and used to search similar Move Commons \cite{movecommons} initiatives, which are initiatives characterized by a set of keywords and other semantic information. The set of keywords of each initiative is considered a significant set of concepts related with it. From this assumption, initiatives with semantically similar keywords are considered to be similar. The problem is interesting because Move Commons initiatives, as many other resources in Internet, are only related with a small set of words, that might be different to others resource keywords but semantically similar. Our tool helps to find relations among initiatives that are similar but with different keywords.  Our proposal assigns a Wikipedia article to each keyword and establish the similarity between two initiatives in terms of the hierarchical Wikipedia categorization of their keywords. Previous works shows the interest of using Wikipedia information as a complete source of semantic information because of its coverage of concepts \cite{Milne_2007} and its adequacy for stablishing semantic relations \cite{Seco04anintrinsic,DBpediaRelationshipFinder}.

Previous works about semantic similarity and search either focus on the semantic relations, loosing efficiency or do not use the available semantic information to improve the results. Our work tries to show an intermediate approach where both semantic information and efficiency are taken into account to obtain a semantic search in a reasonable time.

%%The paper studies the adequacy of measure the similarity among resources characterized with a set of ``natural languaje''  keywords with this technique.
